Day 1: Getting data into shape

Tidyverse world

Day’s objectives

1. Understand workflow, why it is important, and how to do it using Rstudio/R
2. Have an understanding of the tidyverse framework and its key packages
3. Work through an example

Key packages

Example of some workflows

mine

Paul’s

Commonalities

Advantages of workflow

asfd

adsf

Dataset for workshop

link to .

Rstudio

You can find a variety of cheatsheets at https://posit.co/resources/cheatsheets/

Basics

• assume familiar with
• might want to turn off .Rdata so everything is fresh on startup (no issues with previous objects so completely reproducible)

Tips and tricks

• tab
• fills in paths
• hexcode color coating
• code folding (#)
• insert pipe: ctrl+shift+M %>%
• comment: ctrl + shift + C
• find in all files

Programming good technique

• consistent style
• modularize your code (functions - slightly more advanced)

Tidyverse framework

Historical context

• quirky things about R: factors vs character, NAs,

• tibble is data.frame v2.0

  • better printing of the data
  • handles some of the quirky things that caught up people
  • column names

• lubridate fixed date quirks

Importing

Excel files

CSV/Table

Joins

Show code

TRUE

[1] TRUE

knitr::include_graphics(“dplyr.pdf”)

Note: joins = merges (synonyms)

Tips and tricks

• Essential to make sure that the number of rows out matches your expectation
• Almost all my joins are

Restructuring

Show code

TRUE

[1] TRUE

knitr::include_graphics(“tidyr.pdf”)

String manipulation

Show code

TRUE

[1] TRUE

knitr::include_graphics(“stringr.pdf”)

Date/time

Show code

TRUE

[1] TRUE

knitr::include_graphics(“lubridate.pdf”)